Defect classification for specular surfaces based on deflectometry and multi-modal fusion network

•Deep-learning-based method is introduced for the automated defect classification of specular surfaces.•Multi-modal feature fusion network is proposed to further extract the image features from different modalities and increase the performance of defect classification.•Both geometrical and textural defects can be correctly classified.•The benchmark defect dataset for specular surfaces named SpecularDefect9 is generated and released.